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SBIR Phase I:A highly-scalable, rapid, in-season approach to tune a nitrogen model for accurate prediction of a corn crop’s remaining nitrogen need

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2127096
Agency Tracking Number: 2127096
Amount: $256,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: R
Solicitation Number: NSF 21-562
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-01-15
Award End Date (Contract End Date): 2023-02-28
Small Business Information
400 S 4TH ST STE 401210
MINNEAPOLIS, MN 55415
United States
DUNS: 078568757
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Kent Cavender-Bares
 (651) 324-8666
 kbares@rowbot.com
Business Contact
 Kent Cavender-Bares
Phone: (651) 324-8666
Email: kbares@rowbot.com
Research Institution
N/A
Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a novel artificial intelligence technology that enables U.S. corn farmers to optimize nitrogen fertilizer applications based on field characteristics, rainfall and growing conditions. More efficient use of nitrogen fertilizer will reduce the carbon footprint of U.S. agriculture because more than 10% of the energy consumed in the U.S. agricultural sector goes toward the production of nitrogen fertilizer for corn. Because nitrogen fertilizer is a critical driver of both input costs and yield, this technology will improve the profitability of U.S. farmers by reducing input costs and maximizing corn yields. This project may enable corn production that creates less nitrogen fertilizer pollution, which threatens human health, degrades aquatic ecosystems and emits greenhouse gases that contribute to climate change. A key social element of this project is a teaching module for middle-school science students that will blend content on soil science, agronomy, and crop management with the challenges faced by U.S. corn farmers to follow best management practices.This Small Business Innovation Research (SBIR) Phase I project seeks to demonstrate the technical feasibility of using machine learning to evaluate the amount of yellowness on the lowest corn leaves visible in images taken from low-cost cameras mounted on ground robots. Characteristic yellowness on corn leaves is a strong indicator of stress caused by insufficient nitrogen, a key nutrient. A prototype neural network model will be iteratively improved, in part by dramatically increasing the available training imagery over the course of this Phase I project. Imagery will be collected on field trials set up across several Mid-Western states. Preliminary data suggest that the extent of characteristic yellowness is an indicator of accumulated nitrogen stress that is observable only under the canopy and not via airborne sensors. A commercially available nitrogen model will be used to estimate accumulated nitrogen stress across small plots created by manipulating the amount of added nitrogen fertilizer when corn is about 1-2 feet high. Tuning will occur by adjusting key model parameters through simulation until the differences are minimized between observed and modeled accumulated nitrogen stress across a field’s small plots. Parallel software development will improve prototype codes running simulations of a leading nitrogen model, enabling rapid model tuning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

* Information listed above is at the time of submission. *

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